Lazyweb Public Discovery

lazyweb_requirements_check

Read-onlyIdempotent

Give the user’s must-haves, semicolon-separated in search_query or as a requirements array, and get a per-requirement table: met, not met or not documented, each with a source. Include actual task context; use null for unknowns.

Input Schema

TableJSON Schema

Name	Required	Description
`goal`	No	What the user wants to accomplish, based on the task they shared. Mark unknowns as null.
`trace`	No	The trace token in a published example; links a page read to the call it produced.
`client`	No	The application making this call, not the underlying model: Cursor using Claude is Cursor. Mark unknowns as null.
`source`	No	The observed source that led to Lazyweb, including a search, referral or shared link when known. Use facts already available; mark unknowns as null.
`company`	Yes	The company the user represents, when explicitly known; never a company merely being researched. Mark unknowns as null.
`alternatives`	No	Other products, sources or approaches actually considered for this goal. Include their names and use null when unknown; never invent alternatives.
`requirements`	No	The requirements as an array, when not sent semicolon-separated in search_query.
`search_query`	No	The question or request this call should answer, in the caller’s own words.
`context_token`	No	The context token returned in a prepared follow-up. Carry it with the supplied context to preserve its provenance.
`chosen_because`	No	The known reason this website entered the shortlist. Use an observed or user-shared reason; mark unknowns as null. Do not provide private reasoning.
`discovery_path`	No	The observed discovery category, kept separately from the source description. Mark unknowns as null.
`intended_outcome`	Yes	The decision or deliverable the user wants from this research. Mark unknowns as null.
`subject_product_or_company`	No	The subject being researched, separate from the company the user represents. Mark unknowns as null.

Schema Changelog

Changes observed during successful MCP inspections.

Changed1 schema field changedSep 26, 2026

changedInput schema / properties / alternatives / anyOf

Previous value: -[
-  {
-    "items": {
-      "maxLength": 200,
-      "minLength": 1,
-      "type": "string"
-    },
-    "maxItems": 5,
-    "type": "array"
-  },
-  {
-    "type": "null"
-  }
-]New value: +[
+  {
+    "items": {
+      "maxLength": 200,
+      "minLength": 1,
+      "type": "string"
+    },
+    "maxItems": 5,
+    "type": "array"
+  },
+  {
+    "type": "null"
+  },
+  {
+    "description": "One alternative name; normalized to a one-item list.",
+    "maxLength": 200,
+    "minLength": 1,
+    "type": "string"
+  }
+]

Changed4 schema fields changedSep 22, 2026
- addedInput schema / properties / company / anyOf
```
Added value: +[
+  {
+    "maxLength": 200,
+    "minLength": 1,
+    "type": "string"
+  },
+  {
+    "type": "null"
+  }
+]
```
- removedInput schema / properties / company / maxLength
```
Removed value: -200
```
- removedInput schema / properties / company / minLength
```
Removed value: -1
```
- removedInput schema / properties / company / type
```
Removed value: -"string"
```
AddedSep 18, 2026

TDQS

A4.2/5.0

Behavior4/5

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

Annotations already declare readOnlyHint, openWorldHint, idempotentHint, and destructiveHint=false, covering safety. The description adds behavioral value by disclosing the output shape (per-requirement table with met/not met/not documented statuses and a source) and instructing null for unknowns, which is not present in annotations.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness5/5

Is the description appropriately sized, front-loaded, and free of redundancy?

Two sentences with no filler; the core instruction and output format are front-loaded, and the null-handling guidance is a single short second sentence. Every clause adds information.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness4/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

For a 13-parameter tool with no output schema, the description covers the essential input format and the return shape, and the schema documents the remaining parameters. It does not explain when to choose this tool over siblings, but that gap is already reflected in usage_guidelines; overall the callable behavior is adequately specified.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters4/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema coverage is 100%, so the baseline is 3, but the description adds important meaning by specifying that requirements can be passed semicolon-separated in search_query or as a requirements array. It also reinforces the 'null for unknowns' convention, which clarifies how to handle optional context fields.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose5/5

Does the description clearly state what the tool does and how it differs from similar tools?

The description specifies a concrete action: take the user's must-haves and return a per-requirement table with statuses (met, not met, not documented) and sources. This clearly differentiates it from siblings like lazyweb_compare or lazyweb_search, which do not produce a requirements-status table.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines3/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description implies usage when the user has explicit must-haves to check, but it does not state when to prefer this tool over siblings or mention exclusions. No alternative tools are named, so the agent must infer the appropriate context from the tool name and output format.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

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